Social Networks and Instructional Reform in STEM: The Teaching-Research Nexus

John Skvoretz, Katherine Kappelman, Ariel Marcy, Jacob D. McAlpin, Jennifer E. Lewis, John P. Ziker, Karl Mertens, Brittnee Earl, Susan E. Shadle, Brian A. Couch, Stephanie Feola, Luanna B. Prevost, A. Kelly Lane, Blake Whitt, Marilyne Stains

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Instructional reform in STEM aims for the widespread adoption of evidence based instructional practices (EBIPS), practices that implement active learning. Research recognizes that faculty social networks regarding discussion or advice about teaching may matter to such efforts. But teaching is not the only priority for university faculty – meeting research expectations is at least as important and, often, more consequential for tenure and promotion decisions. We see value in understanding how research networks, based on discussion and advice about research matters, relate to teaching networks to see if and how such networks could advance instructional reform efforts. Our research examines data from three departments (biology, chemistry, and geosciences) at three universities that had recently received funding to enhance adoption of EBIPs in STEM fields. We evaluate exponential random graph models of the teaching network and find that (a) the existence of a research tie from one faculty member i to another j enhances the prospects of a teaching tie from i to j, but (b) even though faculty highly placed in the teaching network are more likely to be extensive EBIP users, faculty highly placed in the research network are not, dimming prospects for leveraging research networks to advance STEM instructional reforms.

Original languageEnglish (US)
Pages (from-to)579-600
Number of pages22
JournalInnovative Higher Education
Issue number4
StatePublished - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).


  • Communication networks
  • Evidence based instruction practices
  • STEM instructional reforms, exponential random graph models
  • Teaching and research


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